1. Field of the Invention
The present invention relates to a GeneRalized Autocalibrating Partially Parallel Acquisition (GRAPPA) image reconstruction algorithm, and more particularly to a fast GRAPPA image reconstruction algorithm for magnetic resonance imaging (MRI).
2. Description of the Prior Art
In MRI techniques, imaging speed is a very important parameter. Early examinations often took several hours, and later, as technologies in field intensity, gradient hardware and pulse sequence developed, a relatively large increase in the imaging speed was achieved. However, rapidly switching field gradient and high duty cycle radio frequency (RF) pulses may result in a specific absorption rate (SAR) and heat amount in organs and tissues that exceed human physiological limits. Therefore, a further increase in the imaging speed encounters a limitation.
It has been found that the imaging speed of MRI can be greatly increased with a technique that employs complicated computer image reconstruction algorithms and a coil array. Such techniques are known as parallel imaging techniques. Parallel imaging techniques include SiMultaneous Acquisition of Spatial Harmonics (SMASH), SENSitivity Encoding Parallel Acquisition Techniques (SENSE), GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) etc. Parallel acquisition image reconstruction, an image reconstruction technique for fast acquisitions, utilizes differences in the spatial sensitivities of phased array coils to perform spatial encoding while using the phased array coils to perform signal acquisitions, achieving an imaging speed two to six times higher than that of conventional MRI, or even higher.
Using parallel imaging techniques imposes new requirements on MRI systems. For example, multiple receiving channels and multi-element array coils are needed, coil sensitivity should be calibrated, and special data processing and image reconstruction methods must be adopted.
SMASH is a parallel acquisition reconstruction method that employs coil sensitivity to fit a spatial harmonic function and fills under-sampled data. The algorithm thereof is characterized by data of all channels being added directly to serve as a basis and subject for multi-channel fitting. There is a relatively large error present in the fitting operation of said algorithm, causing severe artifacts in SMASH images as well as low signal-to-noise ratios (SNRs).
GRAPPA is an enhanced SMASH sequence, the GRAPPA algorithm utilizes sampled data of all channels to fit and fill under-sampled data of each of the channels, and performs channel combination operation on fitted full-sampled image of each of the channels, i.e., calculating the sum of squares for each image, adding the sum, and obtaining the extraction of square root of the addition to obtain a resulting image. The GRAPPA algorithm reduces the fitting error and improves the image quality. GRAPPA, however, has a relatively long image reconstruction time since the time needed to fit full channel data is proportional to the number of channels. As the number of channels for parallel acquisitions in MRI devices is increased gradually, this disadvantage of GRAPPA in terms of the image reconstruction speed will become more and more apparent.
Therefore, an urgent problem in the field of MRI is how to provide a fast GRAPPA image reconstruction algorithm that will greatly increase the calculation speed of the GRAPPA image reconstruction and calculate SNR losses of images obtained by parallel acquisition image reconstruction algorithms in the image domain and the frequency domain.